BUSINESS ONTOLOGIES COOPERATION

Mina Ziani, Danielle Boulanger and Guilaine Talens

University of Lyon – Jean Moulin, Modeme Team, 6 Cours Albert Thomas, Lyon, France

Keywords: Ontology alignment, Ontology integration, Hybrid ontology, Mappings, Semantic interoperability.

Abstract: In the context of the cooperation between heterogeneous and distributed information sources, ontologies are

a main issue. To represent shared knowledge, an hybrid domain ontology is designed and to respect each

point of view of different experts, local ontologies are created. Since experts are willing to cooperate,

similarities must be identified to build mappings between the concepts of the different ontologies. We

propose a computer-aided system to allow the experts to choice similarity measures on demand. We apply

this work to the geotechnic domain which involves various businesses.

1 INTRODUCTION

The knowledge management in the context of

heterogeneous and distributed information sources is

a great challenge. The difficulty is to represent all

the domain knowledge specificities and to allow the

cooperation between experts with different points of

view. Ontologies are a promised approach for the

knowledge representation in a formal way.

We propose in this paper an approach to

establish interoperability between knowledge

contained in different local ontologies. An hybrid

ontology (Visser, 2002) is designed. It consists to

describe each information source in a local ontology

and to represent the vocabulary shared by all the

experts.

Each expert community built her own business

ontology. A global ontology is automatically

designed and contains only the common concepts

and properties of all the local ontologies.

To reach cooperation between the several

ontologies and to allow semantic interoperability,

different techniques are used. In particular, ontology

alignment, or matching, is the process of

determining relationships or correspondences

(subsumption, inclusion …) between entities of

different ontologies. The correspondences are also

called alignments. Very often, these relations are

“equivalence relations” discovered through

similarity measures between ontologies’ entities. In

the existing alignment system, these measures are

used, alone or aggregated according to a particular

strategy. This one depends to the domain, the type,

and the use of the ontology. So, we have developed

a generic computer-aided system which guides the

experts to choice the similarity methods and

measures to be used in the alignment process

according to the ontology characteristics.

We applied this work to the geotechnics. It is a

complex domain involving different businesses:

Project management, geologists and chemists…To

design ontology for this domain we use information

contained in the geotechnic referentials and

documents.

We present some tools and frameworks for

merging or aligning ontologies. After, we describe

the hybrid ontology approach to represent

geotechnical knowledge. In order to allow

cooperation between experts, we propose a

framework enable to create mappings. Finally, we

conclude with some perspectives.

2 RELATED WORKS

In OntoDB project, each information source builds

its local ontology from the concepts and relations

contained in a global domain ontology. So, semantic

integration is automatic. This research work utilizes

a strong hypothesis: A database administrator

defines a relevant ontology and he adds the

subsumption relations existing between his local

ontology and the global ontology (Nguyen Xuan,

2006).

OWSICS architecture involves two ontology levels:

214

Ziani M., Boulanger D. and Talens G..

BUSINESS ONTOLOGIES COOPERATION.

DOI: 10.5220/0003651802140219

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 214-219

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The local information sources and the cooperation

(global). Information sources are semantically

explained with corresponding local ontologies. At

the global level reference ontology describes the

domain semantics. A semi-automatic method has

been developed to allow the creation of mappings,

but only between a local ontology and the reference

ontology (Abrouk, 2008).

Except OntoDB, cooperation systems need

ontology alignment. Then, we focus on several tools

to align ontologies. Most of them use terminological,

conceptual or extensional similarity measures and

combine them according to an aggregation strategy.

They differ in their functioning and the interactions

they offer to the users.

PROMPT is a computer-aided system for

comparing, merging, aligning and managing

ontologies. Its alignment and merge module, called

Anchor PROMPT, allows the expert, to find

mapping in the following way: (i) the system

calculates terminological measures to determine an

initial set of similar concepts, (ii) from this list, an

algorithm analyzes paths in the sub-graphs bounded

by these concepts and indicates which classes

frequently appear in the same positions on similar

paths (Noy, 2001).

OLA (OWL Lite Alignment) is a system

implementing an algorithm of ontology alignment

written in OWL. It measures the similarity between

two entities from the similarity between their

characteristics (classes, properties, relations with the

other entities…). The final similarity is the weighted

sum of these similarities. The weights are associated

relatively to the type of entities to be compared. The

algorithm uses a fixed point method with iterations

to improve the similarity of two entities. When there

are no possible improvements, alignments between

two ontologies are proposed (Euzenat, 2004).

AROMA (Rule Ontology Matching Approach

association) is an approach of alignment of

ontologies represented in OWL. It allows

discovering semantic links (subsumption or

equivalence between two entities: Classes or

properties). There are three steps in the process of

alignment: The first one consists in the acquisition

of “relevant terms” for each concept and its

ancestors. These terms, contained in the descriptions

and instances of ontology entities, are extracted with

tools of Natural Language Processing. The second

step allows creating relations of subsumption

between the ontology entities from rules of

association. In the final step, the system analyzes the

relations previously obtained in order to: (i) deduce

the relations of equivalence; (ii) find inconsistences

and eliminate them (iii) delete the redundant

relations; (iv) select the best alignment for every

entity (David, 2009).

More recently, frameworks have appeared in the

ontology alignment systems. They allow multiple

combinations of strategies to calculate the similarity.

For example:

COMA++ (COmbining MAtching) is a generic

system of matching schemas (Do, 2002; Massmannn

2006). This framework allows the importation,

storage, and edition of schemas and produces

alignment algorithms in order to transform or merge

those schemas. It provides an extensible library of

alignments, a module for the combination of the

results and a platform to estimate the various

measures. The user can interact during the matching

process by selecting the measures aggregation

strategy (the average, the weighted sum,…).

MAFRA (MApping FRAmework for distributed

ontologies) is an interactive framework, dynamic

and progressive, for the alignment of ontologies

distributed through the semantic web (Mädche,

2002). The steps of the MAFRA alignment process

are: (i) Importation and standardization of ontologies

to align, (ii) similarities to compute between

elements of different ontologies from a combination

of similarity measures, (iii) formalization of

mappings by establishing "semantic bridges"

between the entities of different ontologies, (iv)

execution of mappings to transform the instances of

source ontology to the instances of a target ontology

based on semantic bridges, and finally the results’

verification.

FOAM (Framework for Ontology Alignment and

Mapping) is a framework used in several systems for

data integration, ontology merging, and ontology

evolutions... The tool implements several measures

and strategies for similarities research and allows to

create mappings between ontologies described in

OWL. The general process of alignment is as

follows: One selects the pairs of entities to be

compared and the characteristics on which to

perform the comparison. The system calculates a

similarity for each pair and for each characteristic.

These results are combined to obtain the final

similarity between each pair of entities. From these

results, FOAM forwards a set of suggestions for

alignment, to be validated by the users (Ehring,

2007).

RiMOM (Risk Minimisation based Ontology

Mapping) is an interactive framework which

implements several strategies to align ontologies

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215

(Tang, 2006); (Li, 2009). The RiMOM’s alignment

process is as follows: The first step consists in

selecting the used measures depending on the

assumed similarity between the ontologies

(terminological or structural). In the second step,

several measures are applied. Then, the results are

combined using linear interpolation function. The

third step is the propagation of the similarities (from

concept to concept, from property to property, from

concept to property). The final step consists in

generating mappings from the results previously

obtained. The process is iterative, with a validation

of results at each iteration.

Tools and frameworks we presented differ in the

measures used and the aggregation strategy. These

systems do not guide user, according to the context,

to use a particular methods to discover similarity. In

addition, these systems do not permit to reuse

previously calculated result.

In order to improve the interactions with the

users, we design a computer-aided system allowed

selected similarity methods according to the

characteristics of the concepts to align. The expert is

guided in the selection of the methods to be

aggregated. Our contribution is to propose a

computer-aided system which calculates similarities

and discovers semantic relations. Similarities and

mappings are stored in databases and can be reused

in order to avoid new computation.

3 PRELIMINARY WORKS

Geotechnics is the science studying the grounds

according to diverse aspects: Mechanics of grounds,

geology, techniques of building…The complexity of

geotechnical domain and the knowledge

heterogeneity imply the sharing and the management

of knowledge to be difficult (Faure, 2007).

To represent knowledge of geotechnical domain,

ontologies are a main issue. The existing concepts in

the domain are too numerous to be represented in a

single ontology (approximately 5000 concepts), so

we propose to design an hybrid ontology.

Each group of experts builds an ontology

representing concepts, properties and instances of

his business: A local ontology. A global ontology is

automatically created by the system to represent a

shared vocabulary.

Initially, we classify the ontologies by level. The

first level corresponds to the ontology sharing the

most common concepts with the others. It represents

the target ontology. The source ontologies are the

other local ontologies.

The concepts and properties of the source

ontology involved in the target ontology are

integrated to the global ontology in the ascending

order of ontology levels.

The result is an ontology represented by a

conceptual graph. This one is verified by the means

of an algorithm of integration (Ziani, 2008) which

allows the deletion of the relations providing cycles

in the global ontology. These last ones are stored in

the local ontologies. Finally, we obtain a consistent

and consensual ontology including the common

concepts and properties of local ontologies and not

the conflicting relations which connect them.

Let a target ontology “Tunnel” containing the

concepts and instances used in tunnel engineering

and a source ontology “Project management”

containing the concepts and instances used by the

experts in project management (cf. figure 1).

Figure 1: Description of the ontologies “Tunnel” and

“Project management”.

The integration program allows deleting the

relation directed from “structure” to “tunnel” in the

global ontology, because there is a cycle between

these two concepts and the relation between them is

not in the target ontology “Tunnel”. This relation is

preserved in the source ontology “Project

management” (cf. figure 2).

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216

Figure 2: Global ontology.

This representation is possible relatively to the

plurality of the geotechnic sub-domains and the

existence of a vocabulary shared by all the experts.

The hybrid ontology contains ten business

ontologies (currently, we have pointed ten differents

sub-domains) and a global one, written in the OWL

language.

Each local ontology is represented as a tree,

simple to implement and destinated to geotechnical

experts. Its size can vary according to the business

(between 100 and 1000 concepts).

4 A FRAMEWORK FOR

ONTOLOGY ALIGNMENT

To permit cooperation business ontologies, we

propose a computer aided system (MOON). It

guides the expert in the process of mapping creation

between concepts of local ontologies (Ziani, 2011).

The architecture of this system is presented in figure

Most of the alignment systems use various

measures of similarity to deduce the similarity

between two entities. The difficulty is to choose the

right measure or the combination of measures to find

the similarity. Our system helps the geotechnical

expert in the process of similarity research between

concepts and generates mappings between them.

When an expert wants to cooperate with another,

he sends to the system a request including two

ontologies: Departure (corresponding to the business

of the expert) and research and, the concept to align

(departure concept) (1). The system loads the two

local and the global ontologies (2). The objective is

to discover the concepts of the research ontology to

align with the concept of the departure ontology.

Then, the system verifies in the similarity database if

there are synonyms for the departure concept (3).

Several methods of similarity measures are

implemented in the framework and can be proposed

to the expert (4). The interest of the framework is to

reuse different implemented measures

(terminological, conceptual...) and to allow the

combination of several similarity measures. The goal

of the system is to give to the geotechnical experts

several measures and to help them choosing the best

ones.

The expert selects the methods and measures (5).

The result is a set of similarities between the

departure concept and the concepts found in the

research ontology. These can be of different types

(equivalence, subsumption) and are stored in the

similarity database (6). Then, they are proposed to

the domain expert (7). The expert can validate or not

these similarities. The semantic links proposed and

the expert names are stored in the mapping database

(8). If the expert knows the concepts to

align, he

directly stores the relation in the mapping database.

This involved the update of the generated mappings

(9).

Figure 3: Architecture of the MOON system.

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When an alignment, between two concepts, is

proposed, the system researches in the mapping

database a relation between the same concepts. If

there is no relation between the concepts, this one is

automatically generated in the ontologies.

Otherwise, there are two possibilities: Either the

same alignment exists, in this case there is no

modification to be brought to the ontologies, or there

is a contradictory alignment: In this case, we cannot

create this latter. The alignment previously created is

deleted. It can be recreated only by a third expert

who confirms one of the existing solutions or by an

expert who modifies the alignment which he has

previously proposed in the mapping database.

The process to guide the experts to choose

similarity measures is explained in the following

paragraphs:

At first, the system calculates the terminological

similarity measures between the departure concept

and all the concepts of the research ontology. When

the terminological similarity measure gives a result,

it means the compared concepts are lexically similar.

After, the system verifies if the concepts are

semantically similar from the global ontology. It

researches and compares the smallest subsuming

concept of the departure concept and the found

concept which exists in the global ontology. If they

are identical, they are considered as potentially

similar. On the contrary, if a link of subsumption in

the global ontology exists, the system deduces a

similarity between them. But if they are no relation

between them in the global ontology, the system

deduces that the compared concepts are not

semantically similar.

Then, if the departure concept contains at least

two attributes, the system proposes to calculate the

similarity measure based on the concept properties.

This measure gives the concepts in ontology

research which have common attributes with the

departure concept.

Then, the system proposes a method based on the

hierarchical structure of ontology: Counting the

edges. It consists in (i) researching the smallest

subsuming concepts of the departure which exists in

the global ontology, (ii) calculating the number of

edges between this common concept and the

departure concept, (iii) selecting all the concepts in

the research ontology from the level N-2 of the

common concept until the level N+2, (iv) suggesting

to the expert the found concepts if their number is

not very important (subordinate or equal to 10).

In addition, to improve these results, the system

proposes extensional methods: Two classes are

similar if they share a subset of instances for

attributes chosen by the expert.

Suppose that the “Project management” ontology

has to cooperate with “Tunnel” ontology. The

discovery of mappings through the system for the

concepts “digging” and “crane” gives the following

results:

For the concept "digging", the terminological

similarity measures between concepts do not give

result. The system calculates the similarity measure

based on the attributes. It finds the same attributes in

the concept “digging” and the concept “earthwork”

containing in the “Tunnel” ontology: “method” and

“ground quantity”. Therefore, it suggests to the

expert to create the relations between them.

For the concept “crane”, the terminological

similarity measures find the concept “truck crane”.

Similarity based on the attributes is not proposed

because the concept “crane” contains less than 2

attributes. Consequently, the system proposes the

method of the edge counting: It researches the

smallest concept subsuming this concept and

existing in the global ontology. It finds “construction

site”. There are 2 edges separating these two

concepts, so it selects all the concepts in the

“Tunnel” ontology from the level 0 (level of the

concept “construction site”) to 4 (4

level from the

concept “construction site”). They find the concepts:

“construction site”, “construction tools”, “truck

crane”, “sealing material” and “tunnel boring

machine”. The number of these concepts is less than

10, the system proposes them as similar concepts

with priority to the concept “truck crane” finding

earlier.

This work offers a guide to an expert in the

process of creating mappings. All the result found

must be stored in the similarity and mapping

databases.

5 CONCLUSIONS

In this article we have presented an approach for the

representation of heterogeneous, distributed

knowledge and the cooperation between experts of

various businesses. We applied this work to the

geotechnical domain.

An hybrid ontology is designed: In the first time,

the business ontologies are conceptualized by a

consensus between several experts in the same sub-

domain. At the end, the global ontology is

automatically built from these local ontologies by an

integration approach. To allow the cooperation

between the experts, we have implemented a

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prototype with a module to guide experts to align

ontologies. In current systems the measure scheduling

is fixed; on the contrary, in our proposition, the

system selects the measures relatively to the ontology

characteristics.

A similarity database stores all the calculated

similarities and a mapping database containing all the

relations validated by the experts. All the stored

measures can be reused to avoid new computations

and so not to perform the process.

Currently, the system of the geotechnical

knowledge management is partially implemented.

The local ontologies and the created mappings

between concepts evolve. They imply modifications

in the global ontology and the generated mappings.

So, the first perspective of this work is to analyze the

consequences of the hybrid ontology evolution and

to propose some solutions to maintain the

consistence of all the ontologies (local and global).

There are diverse systems which manage the

ontology evolution (Stojanovic, 2004; Jaziri, 2010;

Djedidi, 2010). Our future contribution will manage

an hybrid ontology evolution.

The second perspective is to estimate all the

mappings stored in the similarity database. The

interest is to deduce other semantic relations.

Finally, the third perspective is to study the

scalability of the hybrid ontology and the alignments

between concepts.

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